Jacob K Greenberg1, Ranbir Ahluwalia2, Madelyn Hill3, Gabbie Johnson1, Andrew T Hale2, Ahmed Belal4, Shawyon Baygani4, Margaret A Olsen5, Randi E Foraker5, Christopher R Carpenter6, Yan Yan7, Laurie Ackerman4, Corina Noje8, Eric Jackson9, Erin Burns10, Christina M Sayama11, Nathan R Selden11, Shobhan Vachhrajani3, Chevis N Shannon2,12, Nathan Kuppermann13, David D Limbrick1. 1. Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. 2. Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA. 3. Department of Neurological Surgery, Dayton Children's Hospital, Dayton, OH, USA. 4. Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA. 5. Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. 6. Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. 7. Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, USA. 8. Department of Anesthesiology, Johns Hopkins School of Medicine, Baltimore, MD, USA. 9. Department of Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA. 10. Department of Pediatrics, Oregon Health and Science University, Portland, OR, USA. 11. Department of Neurological Surgery, Oregon Health and Science University, Portland, OR, USA. 12. American Society for Reproductive Medicine, University of California Davis, Davis, CA, USA. 13. Department of Emergency Medicine, University of California-Davis, Davis, CA, USA.
Abstract
BACKGROUND: Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. METHODS: We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004-2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. RESULTS: Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as "high risk" for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%-96.6%) but improved specificity (67.4%-81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. CONCLUSIONS: The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population.
BACKGROUND: Clinical decision support (CDS) may improve the postneuroimaging management of children with mild traumatic brain injuries (mTBI) and intracranial injuries. While the CHIIDA score has been proposed for this purpose, a more sensitive risk model may have broader use. Consequently, this study's objectives were to: (1) develop a new risk model with improved sensitivity compared to the CHIIDA model and (2) externally validate the new model and CHIIDA model in a multicenter data set. METHODS: We analyzed children ≤18 years old with mTBI and intracranial injuries included in the PECARN head injury data set (2004-2006). We used binary recursive partitioning to predict the composite outcome of neurosurgical intervention, intubation for > 24 h due to TBI, or death due to TBI. The new model was externally validated in a separate data set that included children treated at any one of six centers from 2006 to 2019. RESULTS: Based on 839 patients from the PECARN data set, a new risk model, the KIIDS-TBI model, was developed that incorporated imaging (e.g., midline shift) and clinical (e.g., Glasgow Coma Scale score) findings. Based on the model-predicted probability of the composite outcome, three cutoffs were evaluated to classify patients as "high risk" for level of care decisions. In the external validation data set consisting of 1,630 patients, the most conservative cutoff (i.e., any predictor present) identified 119 of 119 children with the composite outcome (sensitivity = 100%), but had the lowest specificity (26.3%). The other two decision-making cutoffs had worse sensitivity (94.1%-96.6%) but improved specificity (67.4%-81.3%). The CHIIDA model lacked the most conservative cutoff and otherwise showed the same or slightly worse performance compared to the other two cutoffs. CONCLUSIONS: The KIIDS-TBI model has high sensitivity and moderate specificity for risk stratifying children with mTBI and intracranial injuries. Use of this CDS tool may help improve the safe, resource-efficient management of this important patient population.
Authors: Jacob K Greenberg; Ayodamola Otun; Pyi Theim Kyaw; Christopher R Carpenter; Ross C Brownson; Nathan Kuppermann; David D Limbrick; Randi E Foraker; Po-Yin Yen Journal: Appl Clin Inform Date: 2022-04-27 Impact factor: 2.342
Authors: Jacob K Greenberg; Margaret A Olsen; Gabrielle W Johnson; Ranbir Ahluwalia; Madelyn Hill; Andrew T Hale; Ahmed Belal; Shawyon Baygani; Randi E Foraker; Christopher R Carpenter; Laurie L Ackerman; Corina Noje; Eric M Jackson; Erin Burns; Christina M Sayama; Nathan R Selden; Shobhan Vachhrajani; Chevis N Shannon; Nathan Kuppermann; David D Limbrick Journal: Neurosurgery Date: 2022-03-16 Impact factor: 5.315